TAZ edits

The zonal system for TRMG2 was inherited from the existing model, and Caliper performed a brief review and recommended additional zone splits. These recommendations were focused on addressing two concerns:

  1. Breaking up zones with large employment and households (in base or future).
  2. Adding granularity for universities.

Large zones can create traffic loading problems by generating lots of trips and loading them on the network in a small number of locations. Breaking these into smaller zones allows for more-even loading of traffic and avoids spikes in congestion.

A major concern for universities at the outset is getting transit loading correct. Larger zones capture more trips as “intra-zonal” meaning they never leave the zone and load onto the network. For universities, where trips between classes have a significant impact on transit ridership, it is important that these trips travel between zones. In addition, the added granularity gives more accurate measurements of non-motorized travel times, which is important for the university market.

Caliper made several presentations of proposed changes and took feedback from the stakeholders concerning zone edits. The map below shows the final edits. Scroll/Zoom the map below to review them, and use the layers control box to turn the old and new zone layers off.

Network updates

For each new TAZ, a corresponding centroid node and appropriate centroid connectors were added to the roadway network.

Allocating existing SE data

Placeholder

For now, the SE data is allocated into the new zones using the percent area.

Creating new SE data

Caliper added several new variables to the SE data:

  • Median Income
  • Workers
  • Vehicles
  • % High Earnings
  • Off-campus students

Caliper used the 2014-2018 5-year ACS data to populate the first three of these new attributes and the LEHD LODES7 data for the fourth.

Income

Due to the skewed nature of income distributions, median income is a better approximation of the average than the mean. This information was appended to the socio-economic (SE) data from the Census, but income information is only available at the block group geography level. As a result, all TAZs within a block group have the same median income. In addition, income measures were suppressed by the Census for 88 block groups. For TAZs in these block groups, the regional median income of $65,317 was used.

Workers

Estimates of workers are only available from the ACS at the tract level. It is also important to note that ACS tables that count total workers cannot be used, because they include workers living in group quarters. Instead, total workers in households must be imputed from tables that count households by number of workers.

Without more-disaggregate data to inform the process, the following steps were taken to assign workers to TAZs:

  1. Calculate the average workers per household for the ACS tract.
  2. Use that average for each TAZ within the tract.

In this way, workers were allocated within each tract proportionally based on the number of households.

Vehicles

Vehicle data is available from the ACS at the block group level. Unlike workers, total vehicles can be retrieved for just households; however, the lack of information at the block level means that all TAZs within the same block group are assigned the same number of vehicles per household. Also, some block groups had suppressed vehicle information and the regional average of 1.91 was used.

Percent high earnings

The LEHD Origin-Destination Employment Statistics (LODES) provides many valuable attributes including a breakdown of jobs by earnings group at the block level. Caliper aggregated these block statistics to TRM TAZs in order to calculate the percent of high- and low-earning jobs in each. For this purpose, and due to the limited breakpoints available in the LODES data, the following category definitions were used:

  • High earning: >= $3,333 per month (~$40,000 per year)
  • Low earning: < $3,333 per months

For TAZs with low employment (<30 jobs), the tract percentage was used. This information was added to the SE data table in the field PctHighEarn.

Student housing (off-campus)

The TRMG2 model requires off-campus student housing information as input in the SE data. Total enrollment statistics were collected for the following universities:

  • North Carolina State University (source)
  • University of North Carolina at Chapel Hill (source)
  • Duke University (source)
  • North Carolina Central University (source)

The TRM stakeholders provided counts of students living in dorms for each campus, which allowed Caliper to calculate the total number of off-campus students as shown in the table below.

School Total Enrollment In Dorms Implied Off-Campus
NCSU 31,008 12,424 18,584
UNC 29,468 11,390 18,078
Duke 15,928 5,788 10,140
NCCU 8,096 2,899 5,197

Stakeholders provided Caliper with off-campus student addresses from the same four universities. The NCSU, UNC, and NCCU data sets contained student addresses from all over the country (and world). These addresses are a mix of local students living off campus, distance ed students, and billing addresses (often parent addresses) as shown in the map below.

The histograms below show the number of address points for each university in one-mile bands around the campus. The intensity drops off drastically beyond 10 miles.

For each school, Caliper ignored address points outside 10 miles as shown below.

Finally, the weight of each point within the buffer was factored up to match the total off-campus enrollment for each university. These weights were aggregated by TAZ and added to the SE data table.




Caliper Corporation, 2020